<![CDATA[ IEEE Transactions on Robotics - new TOC ]]>
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TOC Alert for Publication# 8860 2018April 23<![CDATA[Table of Contents]]>342C1C140<![CDATA[IEEE Transactions on Robotics]]>342C2C265<![CDATA[Robust Visual Localization Across Seasons]]>3422893022493<![CDATA[Grasping Without Squeezing: Design and Modeling of Shear-Activated Grippers]]>3423033162415<![CDATA[Elastic Structure Preserving (ESP) Control for Compliantly Actuated Robots]]>3423173354634<![CDATA[The Boundaries of Walking Stability: Viability and Controllability of Simple Models]]>$n$ steps or reach a target within $n$ steps. All such combinations constitute regions in the combined space of states and controls. Farther from the boundaries of these regions, the robot tolerates larger errors and disturbances. Furthermore, for these models, and thus possibly real bipeds, usually if it is possible to avoid falling, it is possible to reach the target, and if it is possible to reach the target, it is possible to do so in two steps.]]>3423363522749<![CDATA[A Novel Robotic Platform for Aerial Manipulation Using Quadrotors as Rotating Thrust Generators]]>$Re^3, times$ S $^2$ with its unactuated dynamics is still internally stable. Experiments are also performed to show the efficacy of the theory.]]>3423533691442<![CDATA[Dynamic Humanoid Locomotion: A Scalable Formulation for HZD Gait Optimization]]>3423703871308<![CDATA[3-D Robust Stability Polyhedron in Multicontact]]>3423884031358<![CDATA[Cooperative Collision Avoidance for Nonholonomic Robots]]>$epsilon$ CCA, for collision avoidance in dynamic environments among interacting agents, such as other robots or humans. Given a preferred motion by a global planner or driver, the method computes a collision-free local motion for a short time horizon, which respects the actuator constraints and allows for smooth and safe control. The method builds on the concept of reciprocal velocity obstacles and extends it to respect the kinodynamic constraints of the robot and account for a grid-based map representation of the environment. The method is best suited for large multirobot settings, including heterogeneous teams of robots, in which computational complexity is of paramount importance and the robots interact with one another. In particular, we consider a set of motion primitives for the robot and solve an optimization in the space of control velocities with additional constraints. Additionally, we propose a cooperative approach to compute safe velocity partitions in the distributed case. We describe several instances of the method for distributed and centralized operation and formulated both as convex and nonconvex optimizations. We compare the different variants and describe the benefits and tradeoffs both theoretically and in extensive experiments with various robotic platforms: robotic wheelchairs, robotic boats, humanoid robots, small unicycle robots, and simulated cars.]]>3424044201633<![CDATA[A Physics-Based Power Model for Skid-Steered Wheeled Mobile Robots]]>3424214331344<![CDATA[Formation Control of Nonholonomic Mobile Robots Without Position and Velocity Measurements]]>3424344461599<![CDATA[Online Identification of Environment Hunt–Crossley Models Using Polynomial Linearization]]>3424474581122<![CDATA[Coordinated Search With Multiple Robots Arranged in Line Formations]]>sweep lines. Sweep lines are used to coordinate the motion of multiple robots and guarantee the detection of any number of arbitrarily fast intruders, even when each robot has a limited sensor footprint. We present a formalization of the problem, coined Line-Clear, which requires the computation of sweep schedules to coordinate the motion of multiple sweep lines using the fewest robots possible. We provide a proof of NP-hardness of the general Line-Clear problem based on results from graph searching. An algorithm to compute sweep schedules for simply connected environments, which additionally guarantees that the cleared area is connected and not recontaminated, is then presented. We analyze its complexity formally and in simulation experiments and present solutions for a number of subproblems required for an implementation of the algorithm. The analysis provides a formal criterion for when the algorithm runs in polynomial time and the experiments indicate that this criterion may be satisfied for most environments in practice.]]>342459473852<![CDATA[Cable-Based Robotic Crane (CBRC): Design and Implementation of Overhead Traveling Cranes Based on Variable Radius Drums]]>$1 %$ throughout the whole working area.]]>3424744851153<![CDATA[Online Approximate Optimal Station Keeping of a Marine Craft in the Presence of an Irrotational Current]]>3424864961114<![CDATA[Ultrahigh-Precision Rotational Positioning Under a Microscope: Nanorobotic System, Modeling, Control, and Applications]]>$text{360}^{circ }$ and in situ twisting characterization of 1-D micro/nanomaterial. This research paves a new avenue for the ultrahigh rotational positioning at microscopy environment, which is expected to generate a long-term impact on the micro/nanofields, such as microscopy imaging, material characterization, and so on.]]>3424975074923<![CDATA[Adaptive Gain Control Strategy for Constant Optical Flow Divergence Landing]]>3425085161368<![CDATA[Controlling Noncooperative Herds with Robotic Herders]]>3425175251719<![CDATA[<inline-formula><tex-math notation="LaTeX">$varepsilon ^{star }$</tex-math></inline-formula>: An Online Coverage Path Planning Algorithm]]>$varepsilon ^{star }$ , for online coverage path planning of unknown environment. The algorithm is built upon the concept of an Exploratory Turing Machine (ETM), which acts as a supervisor to the autonomous vehicle to guide it with adaptive navigation commands. The ETM generates a coverage path online using Multiscale Adaptive Potential Surfaces (MAPS), which are hierarchically structured and dynamically updated based on sensor information. The $varepsilon ^{star }$-algorithm is computationally efficient, guarantees complete coverage, and does not suffer from the local extrema problem. Its performance is validated by 1) high-fidelity simulations on Player/Stage and 2) actual experiments in a laboratory setting on autonomous vehicles.]]>3425265332805<![CDATA[Full-Pose Tracking Control for Aerial Robotic Systems With Laterally Bounded Input Force]]>underactuated and fully actuated platforms, and guarantees at least the position tracking in the case of an unfeasible full-pose reference trajectory. Several experimental tests are presented that clearly show the approach practicability and the sharp improvement with respect to state of the art.]]>3425345412138<![CDATA[Comparative Peg-in-Hole Testing of a Force-Based Manipulation Controlled Robotic Hand]]>342542549745<![CDATA[Introducing IEEE Collabratec]]>3425505501914<![CDATA[IEEE Global History Network]]>342551551874<![CDATA[Imagine a community hopeful for the future]]>3425525521455<![CDATA[blank page]]>342C3C32<![CDATA[INFORMATION FOR AUTHORS]]>342C4C461